GEO audit: how your SaaS site shows up in ChatGPT, Perplexity, and Gemini answers (not just Google)

What a Naavim GEO audit actually does

1. 01

   Run 60 buyer-intent prompts across four engines

   We script ChatGPT, Perplexity, Gemini, and Google AI Overviews with the exact queries your ICP types: category queries, jobs-to-be-done queries, comparison + alternative queries, and integration queries. For each prompt we log who gets cited verbatim, who gets summarised without attribution, and who gets skipped entirely, then graph the citation network so you can see exactly which competitor pages are stealing your demos.

2. 02

   Tear down your structured-data + content gaps

   JSON-LD coverage (Organization, SoftwareApplication, FAQPage, BreadcrumbList, HowTo), schema accuracy against Schema.org and Google Rich Results validators, content-chunking depth, citation-worthy explicit numerical claims, alt-text for image carousels, internal-link anchor specificity, page freshness signals. The eight signals AI crawlers weight most when picking citation sources for an answer.

3. 03

   Ship the schema, content, and citation fixes

   We rewrite your top 10 commercial-intent pages for citability (short answer-shaped paragraphs, sourced numerical claims, FAQ blocks), deploy all five JSON-LD blocks, file the structured-data updates with Google + Bing, and seed the third-party citations on Reddit, Substack, G2, Capterra, and Indian SaaS round-ups that LLM crawlers learn from over time. We re-audit at 30 and 90 days to measure citation lift and tune the remaining gaps.

• Category queries: "best X for Y in India"

  Comparison hubs with verifiable feature tables, schema-marked product cards, and Indian-context evidence (DPDP Act compliance, INR pricing tiers, Indian payment rails, GST-invoice support). These are the highest-citation-value pages on most SaaS sites and the most consistently under-built.

  See our WATI vs Interakt vs AiSensy GEO playbook
• Integration queries: "does X work with Razorpay / Zoho / Tally"

  Per-integration pages with HowTo schema, code snippets in the buyer's actual stack, and Indian-stack screenshots that answer engines cite verbatim when buyers ask about specific Indian tooling fit.

  Read the n8n automation agency playbook
• Comparison + alternative queries

  Side-by-side pages that pass the Perplexity citation test: sourced rows, neutral tone, explicit weaknesses called out, and disclosure of who you do and don't resell. Neutral comparison content gets disproportionate citation weight because answer engines value perceived objectivity.

  See the CRM vs WhatsApp vs email comparison
• ROI + benchmark queries: "how much does X cost in India"

  Calculators and benchmark pages that surface numerical answers AI engines prefer to direct-quote. INR cost ranges, conversion-rate benchmarks, and ROI projections all rank for high-intent buyer queries.

  See the missed lead cost calculator
• Jobs-to-be-done queries: "how to do X for Indian businesses"

  Long-form HowTo pages with step-by-step JSON-LD, screenshots, and India-specific gotchas (GST, TDS, DPDP consent, RBI guidelines) that the global articles miss entirely. These pages own the long-tail and feed the top-of-funnel citation graph.

  Read the AI workflow automation playbook
• Hindi + Hinglish discovery

  AI engines now answer Hindi queries in Hindi and prefer Devanagari content for those answers. We add a Hinglish title-tag layer and Devanagari heading mirrors so your category gets cited across both language pools. A single-language SaaS site is invisible to roughly 30% of Indian B2B buyer queries.

  See the Indian D2C GEO playbook

GEO for Indian SaaS: the questions buyers actually ask

• What is GEO (Generative Engine Optimization) and how is it different from SEO?

  SEO optimises for Google's ranked link list: ten blue links, you fight for the top three. GEO optimises for the answer engines (ChatGPT, Perplexity, Gemini, Google AI Overviews) that synthesise a single answer and cite 2-5 sources. The mechanics overlap (clean schema, fast pages, authoritative content all still matter), but GEO weights structured data, explicit numerical claims with sources, third-party mentions on Reddit and G2, and short answer-shaped content chunks far more heavily than classical SEO does. Most Indian SaaS teams are still optimising for the blue links and missing the citation game entirely.
• How do ChatGPT, Perplexity, and Gemini actually crawl an Indian SaaS site?

  Perplexity and Gemini have live web access via their own crawlers and re-crawl on every relevant query, so your page can be cited within minutes of going live. ChatGPT browses via OAI-SearchBot and Bing's index, so updates take 24-72 hours to surface. All four engines prefer pages with explicit JSON-LD (Organization, SoftwareApplication, FAQPage), short answer-shaped paragraphs (2-4 sentences per chunk), and authoritative third-party mentions. Indian sites also get downranked when content reads as keyword-stuffed or thin, a common pattern in the local SaaS niche we audit.
• Which structured-data schema types matter most for SaaS GEO?

  Five blocks do most of the work, and most Indian SaaS sites ship zero or one of them. Organization (root identity, links your social handles + G2 profile), SoftwareApplication (product entity with applicationCategory, operatingSystem, offers + pricing range), FAQPage (cited verbatim by answer engines for direct-answer queries), BreadcrumbList (hierarchy signal that lifts deep-page citation odds), and HowTo (cited as step-by-step answers for integration and onboarding queries). The audit deploys all five, validates them in Google's Rich Results Test, and re-checks via Schema.org's validator before go-live.
• What ranking signals do ChatGPT and Perplexity weight that classical Google search doesn't?

  Three big ones. First, third-party citation density: being mentioned on Reddit, Substack, Indian SaaS round-ups, G2, Capterra, and tools-comparison content carries disproportionate weight because answer engines treat them as independent verification. Second, explicit numerical claims with sourceLabel-style attribution: answer engines prefer quotable numbers over hand-wavy adjectives. Third, content freshness signals: answer engines down-weight pages that haven't been updated in 12+ months because the underlying knowledge cutoff matters. We address all three in the 90-day plan.
• Are there India-specific GEO gotchas: Hindi, .in domains, language fallback?

  Yes. ChatGPT and Gemini both answer Hindi and Hinglish queries in the asked language, and they prefer Devanagari content when generating Hindi answers, but most .in SaaS sites only ship English content. We add Hinglish title-tag layers and Devanagari heading mirrors for the top 10 commercial-intent pages so your category gets cited in both language pools. The .in TLD itself carries no penalty for global answers; what actually matters is your hreflang configuration and the natural language of your H1 + first paragraph.
• How long does a full GEO audit take, and what does the deliverable look like?

  Two weeks end-to-end. Week 1 we run 60 buyer-intent prompts across all four engines and log the citation graph (who gets cited, who gets summarised, who gets skipped). Week 2 we tear down your schema, content, and third-party mentions, then ship a fix-list memo prioritised by the citation-uplift expected per fix. You walk away with a 25-30 page report covering current visibility, schema gaps, content rewrites, and a 90-day implementation roadmap. The fix-implementation engagement is a separate scope, sized by the audit findings.
• When does the dedicated SEO + GEO grader tool ship, and how does it differ from the audit?

  The self-serve grader ships in Phase 3 (Q3 2026). It will run 20 buyer-intent prompts across the four engines and return a 0-100 GEO score in under 90 seconds, plus a fix-path PDF emailed to you. The paid audit goes much deeper: 60 prompts, full schema teardown, content rewrites, third-party citation seeding (Reddit + G2 + Capterra outreach), and a 90-day re-check measuring citation lift. Use the automation advisor today as the closest free proxy for the AI-readiness side of the equation.